Overview

This notebook gives a general overview of the features included in the dataset.

Show imports Hide imports

%load_ext autoreload
%autoreload 2
import os

import dimcat as dc
import pandas as pd
import plotly.express as px
from dimcat import filters, plotting
from IPython.display import display

import utils

RESULTS_PATH = os.path.abspath(os.path.join(utils.OUTPUT_FOLDER, "overview"))
os.makedirs(RESULTS_PATH, exist_ok=True)


def make_output_path(
    filename: str,
    extension=None,
    path=RESULTS_PATH,
) -> str:
    return utils.make_output_path(filename=filename, extension=extension, path=path)


def save_figure_as(
    fig, filename, formats=("png", "pdf"), directory=RESULTS_PATH, **kwargs
):
    if formats is not None:
        for fmt in formats:
            plotting.write_image(fig, filename, directory, format=fmt, **kwargs)
    else:
        plotting.write_image(fig, filename, directory, **kwargs)

Loading data

D = utils.get_dataset("couperin_concerts", corpus_release="v2.3")
package = D.inputs.get_package()
package_info = package._package.custom
git_tag = package_info.get("git_tag")
utils.print_heading("Data and software versions")
print("François Couperin – Concerts Royaux version v2.3")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D

Data and software versions
--------------------------

François Couperin – Concerts Royaux version v2.3
Datapackage 'couperin_concerts' @ v2.3
dimcat version 3.4.0

Dataset
=======
{'inputs': {'basepath': None,
            'packages': {'couperin_concerts': ["'couperin_concerts.measures' (MuseScoreFacetName.MuseScoreMeasures)",
                                               "'couperin_concerts.notes' (MuseScoreFacetName.MuseScoreNotes)",
                                               "'couperin_concerts.expanded' (MuseScoreFacetName.MuseScoreHarmonies)",
                                               "'couperin_concerts.chords' (MuseScoreFacetName.MuseScoreChords)",
                                               "'couperin_concerts.metadata' (FeatureName.Metadata)"]}},
 'outputs': {'basepath': None, 'packages': {}},
 'pipeline': []}

filtered_D = filters.HasHarmonyLabelsFilter(keep_values=[True]).process(D)
all_metadata = filtered_D.get_metadata()
assert len(all_metadata) > 0, "No pieces selected for analysis."
all_metadata

		TimeSig	KeySig	last_mc	last_mn	length_qb	last_mc_unfolded	last_mn_unfolded	length_qb_unfolded	volta_mcs	all_notes_qb	...	has_drumset	ambitus	path	originalFormat	staff_1_instrument	staff_1_ambitus	staff_2_instrument	staff_2_ambitus	staff_3_instrument	staff_3_ambitus
corpus	piece
couperin_concerts	c01n01_prelude	{1: '4/4'}	{1: 1}	25	23	98.0	25	23	98.0	()	219.00	...	0	36-84 (C2-C6)	MS3/c01n01_prelude.mscx	xml	Instrument 1	59-84 (B3-C6)	Instrument 1	36-57 (C2-A3)	<NA>	<NA>
	c01n02_allemande	{1: '4/4'}	{1: 1}	20	18	72.0	40	36	144.0	()	168.50	...	0	31-83 (G1-B5)	MS3/c01n02_allemande.mscx	xml	Instrument 1	59-83 (B3-B5)	Instrument 1	31-66 (G1-F#4)	<NA>	<NA>
	c01n03_sarabande	{1: '3/4'}	{1: -2}	30	28	90.0	56	56	168.0	(8], [9]], [[29], [30)	237.75	...	0	31-81 (G1-A5)	MS3/c01n03_sarabande.mscx	xml	Instrument 1	58-81 (Bb3-A5)	Instrument 1	31-67 (G1-G4)	<NA>	<NA>
	c01n04_gavotte	{1: '2/2'}	{1: -2}	18	14	60.0	32	28	112.0	(5], [6]], [[17], [18)	135.00	...	0	38-79 (D2-G5)	MS3/c01n04_gavotte.mscx	xml	Instrument 1	58-79 (Bb3-G5)	Instrument 1	38-57 (D2-A3)	<NA>	<NA>
	c01n05_gigue	{1: '6/8'}	{1: 1}	33	30	93.5	62	60	180.0	(11], [12]], [[32], [33)	194.00	...	0	38-83 (D2-B5)	MS3/c01n05_gigue.mscx	xml	Instrument 1	59-83 (B3-B5)	Instrument 1	38-64 (D2-E4)	<NA>	<NA>
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	parnasse_03	{1: '4/4'}	{1: 2}	59	58	235.0	59	58	235.0	()	655.00	...	0	38-86 (D2-D6)	MS3/parnasse_03.mscx	<NA>	Piano	61-86 (C#4-D6)	Piano	57-84 (A3-C6)	Piano	38-67 (D2-G4)
	parnasse_04	{1: '3/8'}	{1: 2}	57	57	84.5	57	57	84.5	()	196.62	...	0	40-83 (E2-B5)	MS3/parnasse_04.mscx	<NA>	Piano	62-83 (D4-B5)	Piano	57-83 (A3-B5)	Piano	40-69 (E2-A4)
	parnasse_05	{1: '4/4'}	{1: 2}	27	26	107.5	27	26	107.5	()	276.00	...	0	36-83 (C2-B5)	MS3/parnasse_05.mscx	<NA>	Piano	59-83 (B3-B5)	Piano	55-83 (G3-B5)	Piano	36-66 (C2-F#4)
	parnasse_06	{1: '3/8'}	{1: 2}	32	32	48.0	32	32	48.0	()	121.63	...	0	38-83 (D2-B5)	MS3/parnasse_06.mscx	<NA>	Piano	65-83 (E#4-B5)	Piano	64-83 (E4-B5)	Piano	38-66 (D2-F#4)
	parnasse_07	{1: '4/4'}	{1: 2}	53	53	212.0	53	53	212.0	()	514.00	...	0	38-86 (D2-D6)	MS3/parnasse_07.mscx	<NA>	Piano	61-84 (C#4-C6)	Piano	59-86 (B3-D6)	Piano	38-69 (D2-A4)

84 rows × 53 columns

mean_composition_years = utils.corpus_mean_composition_years(all_metadata)
chronological_order = mean_composition_years.index.to_list()
corpus_colors = dict(zip(chronological_order, utils.CORPUS_COLOR_SCALE))
corpus_names = {
    corp: utils.get_corpus_display_name(corp) for corp in chronological_order
}
chronological_corpus_names = list(corpus_names.values())
corpus_name_colors = {
    corpus_names[corp]: color for corp, color in corpus_colors.items()
}

mean_composition_years

corpus
couperin_concerts    1723.380952
Name: mean_composition_year, dtype: float64

Composition dates

This section relies on the dataset’s metadata.

valid_composed_start = pd.to_numeric(all_metadata.composed_start, errors="coerce")
valid_composed_end = pd.to_numeric(all_metadata.composed_end, errors="coerce")
print(
    f"Composition dates range from {int(valid_composed_start.min())} {valid_composed_start.idxmin()} "
    f"to {int(valid_composed_end.max())} {valid_composed_end.idxmax()}."
)

Composition dates range from 1722 ('couperin_concerts', 'c01n01_prelude') to 1724 ('couperin_concerts', 'c05n01_prelude').

Mean composition years per corpus

def make_summary(metadata_df):
    piece_is_annotated = metadata_df.label_count > 0
    return metadata_df[piece_is_annotated].copy()

Show source Hide source

summary = make_summary(all_metadata)
bar_data = pd.concat(
    [
        mean_composition_years.rename("year"),
        summary.groupby(level="corpus").size().rename("pieces"),
    ],
    axis=1,
).reset_index()

N = len(summary)
fig = px.bar(
    bar_data,
    x="year",
    y="pieces",
    color="corpus",
    color_discrete_map=corpus_colors,
    title=f"Temporal coverage of the {N} annotated pieces in the Distant Listening Corpus",
)
fig.update_traces(width=5)
fig.update_layout(**utils.STD_LAYOUT)
fig.update_traces(width=5)
save_figure_as(fig, "pieces_timeline_bars")
fig.show()

summary

		TimeSig	KeySig	last_mc	last_mn	length_qb	last_mc_unfolded	last_mn_unfolded	length_qb_unfolded	volta_mcs	all_notes_qb	...	has_drumset	ambitus	path	originalFormat	staff_1_instrument	staff_1_ambitus	staff_2_instrument	staff_2_ambitus	staff_3_instrument	staff_3_ambitus
corpus	piece
couperin_concerts	c01n01_prelude	{1: '4/4'}	{1: 1}	25	23	98.0	25	23	98.0	()	219.00	...	0	36-84 (C2-C6)	MS3/c01n01_prelude.mscx	xml	Instrument 1	59-84 (B3-C6)	Instrument 1	36-57 (C2-A3)	<NA>	<NA>
	c01n02_allemande	{1: '4/4'}	{1: 1}	20	18	72.0	40	36	144.0	()	168.50	...	0	31-83 (G1-B5)	MS3/c01n02_allemande.mscx	xml	Instrument 1	59-83 (B3-B5)	Instrument 1	31-66 (G1-F#4)	<NA>	<NA>
	c01n03_sarabande	{1: '3/4'}	{1: -2}	30	28	90.0	56	56	168.0	(8], [9]], [[29], [30)	237.75	...	0	31-81 (G1-A5)	MS3/c01n03_sarabande.mscx	xml	Instrument 1	58-81 (Bb3-A5)	Instrument 1	31-67 (G1-G4)	<NA>	<NA>
	c01n04_gavotte	{1: '2/2'}	{1: -2}	18	14	60.0	32	28	112.0	(5], [6]], [[17], [18)	135.00	...	0	38-79 (D2-G5)	MS3/c01n04_gavotte.mscx	xml	Instrument 1	58-79 (Bb3-G5)	Instrument 1	38-57 (D2-A3)	<NA>	<NA>
	c01n05_gigue	{1: '6/8'}	{1: 1}	33	30	93.5	62	60	180.0	(11], [12]], [[32], [33)	194.00	...	0	38-83 (D2-B5)	MS3/c01n05_gigue.mscx	xml	Instrument 1	59-83 (B3-B5)	Instrument 1	38-64 (D2-E4)	<NA>	<NA>
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	parnasse_03	{1: '4/4'}	{1: 2}	59	58	235.0	59	58	235.0	()	655.00	...	0	38-86 (D2-D6)	MS3/parnasse_03.mscx	<NA>	Piano	61-86 (C#4-D6)	Piano	57-84 (A3-C6)	Piano	38-67 (D2-G4)
	parnasse_04	{1: '3/8'}	{1: 2}	57	57	84.5	57	57	84.5	()	196.62	...	0	40-83 (E2-B5)	MS3/parnasse_04.mscx	<NA>	Piano	62-83 (D4-B5)	Piano	57-83 (A3-B5)	Piano	40-69 (E2-A4)
	parnasse_05	{1: '4/4'}	{1: 2}	27	26	107.5	27	26	107.5	()	276.00	...	0	36-83 (C2-B5)	MS3/parnasse_05.mscx	<NA>	Piano	59-83 (B3-B5)	Piano	55-83 (G3-B5)	Piano	36-66 (C2-F#4)
	parnasse_06	{1: '3/8'}	{1: 2}	32	32	48.0	32	32	48.0	()	121.63	...	0	38-83 (D2-B5)	MS3/parnasse_06.mscx	<NA>	Piano	65-83 (E#4-B5)	Piano	64-83 (E4-B5)	Piano	38-66 (D2-F#4)
	parnasse_07	{1: '4/4'}	{1: 2}	53	53	212.0	53	53	212.0	()	514.00	...	0	38-86 (D2-D6)	MS3/parnasse_07.mscx	<NA>	Piano	61-84 (C#4-C6)	Piano	59-86 (B3-D6)	Piano	38-69 (D2-A4)

84 rows × 53 columns

Composition years histogram

Show source Hide source

hist_data = summary.reset_index()
hist_data.corpus = hist_data.corpus.map(corpus_names)
fig = px.histogram(
    hist_data,
    x="composed_end",
    color="corpus",
    labels=dict(
        composed_end="decade",
        count="pieces",
    ),
    color_discrete_map=corpus_name_colors,
    title=f"Temporal coverage of the {N} annotated pieces in the Distant Listening Corpus",
)
fig.update_traces(xbins=dict(size=10))
fig.update_layout(**utils.STD_LAYOUT)
fig.update_legends(font=dict(size=16))
save_figure_as(fig, "pieces_timeline_histogram", height=1250)
fig.show()

Dimensions

Overview

def make_overview_table(groupby, group_name="pieces"):
    n_groups = groupby.size().rename(group_name)
    absolute_numbers = dict(
        measures=groupby.last_mn.sum(),
        length=groupby.length_qb.sum(),
        notes=groupby.n_onsets.sum(),
        labels=groupby.label_count.sum(),
    )
    absolute = pd.DataFrame.from_dict(absolute_numbers)
    absolute = pd.concat([n_groups, absolute], axis=1)
    sum_row = pd.DataFrame(absolute.sum(), columns=["sum"]).T
    absolute = pd.concat([absolute, sum_row])
    return absolute


absolute = make_overview_table(summary.groupby("workTitle"))
# print(absolute.astype(int).to_markdown())
absolute.astype(int)

	pieces	measures	length	notes	labels
Concert Royal no. 1	6	137	491	1859	480
Concert Royal no. 2	5	248	711	2500	597
Concert Royal no. 3	8	300	865	3565	805
Concert Royal no. 4	7	242	780	2743	652
Le Parnasse, ou L'Apothéose de Corelli	7	306	941	4825	957
Nouveau Concert, ou Les Goûts-Réunis no. 10	4	124	431	1596	364
Nouveau Concert, ou Les Goûts-Réunis no. 11	8	254	964	2890	855
Nouveau Concert, ou Les Goûts-Réunis no. 14	4	122	408	1645	466
Nouveau Concert, ou Les Goûts-Réunis no. 5	5	205	617	2014	529
Nouveau Concert, ou Les Goûts-Réunis no. 6	5	164	566	2133	590
Nouveau Concert, ou Les Goûts-Réunis no. 7	6	171	637	2364	602
Nouveau Concert, ou Les Goûts-Réunis no. 8	11	420	1620	3542	939
Nouveau Concert, ou Les Goûts-Réunis no. 9	8	252	800	3211	919
sum	84	2945	9834	34887	8755

def summarize_dataset(D):
    all_metadata = D.get_metadata()
    summary = make_summary(all_metadata)
    return make_overview_table(summary.groupby(level=0))


corpus_summary = summarize_dataset(D)
print(corpus_summary.astype(int).to_markdown())

|                   |   pieces |   measures |   length |   notes |   labels |
|:------------------|---------:|-----------:|---------:|--------:|---------:|
| couperin_concerts |       84 |       2945 |     9834 |   34887 |     8755 |
| sum               |       84 |       2945 |     9834 |   34887 |     8755 |

Measures

all_measures = D.get_feature("measures")
print(
    f"{len(all_measures.index)} measures over {len(all_measures.groupby(level=[0,1]))} files."
)
all_measures.head()

3394 measures over 91 files.

			mc	mn	quarterbeats	duration_qb	keysig	timesig	act_dur	mc_offset	numbering_offset	dont_count	barline	breaks	repeats	next	volta	markers	jump_bwd	jump_fwd	play_until
corpus	piece	i
couperin_concerts	c01n01_prelude	0	1	0	0	2.0	1	4/4	1/2	1/2	<NA>	1	<NA>	<NA>	firstMeasure	(2,)	<NA>	<NA>	<NA>	<NA>	<NA>
		1	2	1	2	4.0	1	4/4	1	0	<NA>	<NA>	<NA>	<NA>	<NA>	(3,)	<NA>	<NA>	<NA>	<NA>	<NA>
		2	3	2	6	4.0	1	4/4	1	0	<NA>	<NA>	<NA>	<NA>	<NA>	(4,)	<NA>	<NA>	<NA>	<NA>	<NA>
		3	4	3	10	4.0	1	4/4	1	0	<NA>	<NA>	<NA>	line	<NA>	(5,)	<NA>	<NA>	<NA>	<NA>	<NA>
		4	5	4	14	4.0	1	4/4	1	0	<NA>	<NA>	<NA>	<NA>	<NA>	(6,)	<NA>	<NA>	<NA>	<NA>	<NA>

all_measures.get_default_analysis().plot_grouped()

Harmony labels

All symbols, independent of the local key (the mode of which changes their semantics).

try:
    all_annotations = D.get_feature("harmonylabels").df
except Exception:
    all_annotations = pd.DataFrame()
n_annotations = len(all_annotations.index)
includes_annotations = n_annotations > 0
if includes_annotations:
    display(all_annotations.head())
    print(f"Concatenated annotation tables contains {all_annotations.shape[0]} rows.")
    no_chord = all_annotations.root.isna()
    if no_chord.sum() > 0:
        print(
            f"{no_chord.sum()} of them are not chords. Their values are:"
            f" {all_annotations.label[no_chord].value_counts(dropna=False).to_dict()}"
        )
    all_chords = all_annotations[~no_chord].copy()
    print(
        f"Dataset contains {all_chords.shape[0]} tokens and {len(all_chords.chord.unique())} types over "
        f"{len(all_chords.groupby(level=[0,1]))} documents."
    )
    all_annotations["corpus_name"] = all_annotations.index.get_level_values(0).map(
        utils.get_corpus_display_name
    )
    all_chords["corpus_name"] = all_chords.index.get_level_values(0).map(
        utils.get_corpus_display_name
    )
else:
    print("Dataset contains no annotations.")

			mc	mn	quarterbeats	duration_qb	mc_onset	mn_onset	timesig	staff	voice	volta	...	numeral_or_applied_to_numeral	intervals_over_bass	intervals_over_root	scale_degrees	scale_degrees_and_mode	scale_degrees_major	scale_degrees_minor	globalkey	localkey	chord
corpus	piece	i
couperin_concerts	c01n01_prelude	0	1	0	0	2.00	0	1/2	4/4	1	1	<NA>	...	I	(M3, P5)	(M3, P5)	(1, 3, 5)	(1, 3, 5), major	(1, 3, 5)	(1, #3, 5)	G	I	I
		1	2	1	2	2.00	0	0	4/4	1	1	<NA>	...	V	(M3, P5)	(M3, P5)	(5, 7, 2)	(5, 7, 2), major	(5, 7, 2)	(5, #7, 2)	G	I	V
		2	2	1	4	0.50	1/2	1/2	4/4	1	1	<NA>	...	I	(m3, m6)	(M3, P5)	(3, 5, 1)	(3, 5, 1), major	(3, 5, 1)	(#3, 5, 1)	G	I	I6
		3	2	1	9/2	0.50	5/8	5/8	4/4	1	1	<NA>	...	I	(M3, P5)	(M3, P5)	(1, 3, 5)	(1, 3, 5), major	(1, 3, 5)	(1, #3, 5)	G	I	I
		4	2	1	5	0.75	3/4	3/4	4/4	1	1	<NA>	...	V	(P4, P5)	(P4, P5)	(5, 1, 2)	(5, 1, 2), major	(5, 1, 2)	(5, 1, 2)	G	I	V(4)

5 rows × 51 columns

Concatenated annotation tables contains 8376 rows.
Dataset contains 8376 tokens and 241 types over 84 documents.